Computers can’t compare meaning directly, only numbers. An embedding model converts a piece of text into a long list of numbers, a vector, positioned so that content with similar meaning ends up mathematically close together, and content with different meaning ends up far apart, regardless of whether the exact words match.
This is what lets a search for “cancel my subscription” find a document titled “how to end your plan,” even though they share almost no words in common. It’s the mechanism underneath modern semantic search and is the first step in most retrieval-augmented generation systems: documents get embedded once, stored, and then compared against the embedding of each new query.
The quality of your embeddings, how well they capture the specific meaning that matters in your domain, sets a ceiling on how good your retrieval can be. No amount of downstream prompt tuning fixes a retrieval system built on the wrong embeddings for the content it’s searching.